| 05 Aug 2026
Magnitude and controls of snow sublimation at a high-elevation Swiss Alpine site
Abstract. Surface snow sublimation remains poorly quantified in the European Alps, with previous modeling studies estimating a broad range of winter surface sublimation losses (10 to 180 mm w.e.). This creates large uncertainties in the partitioning of snow ablation between sublimation and snowmelt, with significant implications for downstream water availability, as surface mass losses through sublimation directly reduce spring meltwater availability. Field observations used to quantify surface snow sublimation throughout a winter season remain scarce due to the logistical challenges posed by high-elevation Alpine environments. To address this, we estimated surface snow sublimation using the eddy-covariance method over one full winter season (November 2024 to June 2025) at the Weissfluhjoch research site (2536 m a.s.l) in the Eastern Swiss Alps by measuring water vapor fluxes with an integrated open-path gas analyzer and sonic anemometer (IRGASON). Partial correlation analyses and explainable machine learning (XGBoost model with Shapley Additive Explanations) were used to quantify the relative importance of different meteorological variables on modeled sublimation. Partial correlation analyses and explainable machine learning (XGBoost model with Shapley Additive Explanations) were used to quantify the relative importance of different meteorological variables on modeled sublimation. Over the 2024–25 winter season, cumulative net surface sublimation was 24.7 ± 18.3 mm, equivalent to 5.7 ± 4.2 % of maximum snow water equivalent, with an average daily sublimation rate of 0.15 mm d−1. Nearly half of cumulative surface sublimation occurred after peak snow height was reached, between March 31, 2025 and June 1, 2025. Vapor pressure gradient and wind speed emerged as the dominant controls on surface sublimation across both statistical and machine learning approaches, while net shortwave radiation became increasingly important following peak snow height. In contrast, air temperature and snow surface temperature showed little independent predictive power once covariance with other variables was accounted for. Our findings highlight the importance of continuous eddy-covariance measurements for quantifying snow sublimation and provide one of the first winter-season estimates of surface snow sublimation in the European Alps.
Review of egusphere-2026-4268: “Magnitude and controls of snow sublimation at a high-elevation Swiss Alpine site”
I have reviewed the paper “Magnitude and controls of snow sublimation at a high-elevation Swiss Alpine site” by Anglin et al. for publication in The Cryosphere. The study presents results from a field-based experiment at an alpine study site in the European Alps where eddy covariance instrumentation was combined with meteorological and snow monitoring data to quantify surface snow sublimation fluxes over one winter season. Furthermore, the dominant controls on surface sublimation fluxes were assessed using partial correlation and machine learning approaches. Results highlight vapor pressure deficit and wind speed as the dominate drivers of sublimation losses and an average snow season sublimation rate of 0.15 mm day-1 with greater fluxes occurring following peak snow height. I believe this paper addresses an important topic and clearly demonstrates a contribution to the field that is relevant to the readership of The Cryosphere. The paper is clearly organized and well written overall. My comments on the paper are outlined below:
General comments:
1) One limitation of the current study is that only one year of data are available for analysis. So, the natural question is: what would we expect the year-to-year variability of sublimation fluxes to be with respect to the one year of data presented? The authors do a nice job of highlighting results from previous studies in the discussion relevant to interannual variability of sublimation. Furthermore, they put the 2024-25 winter in context from a snow accumulation and snow cover duration perspective. However, given the history of met data collected at this site, did you consider looking at some of the historical data of the identified drivers of sublimation (e.g., VPD, wind speed, radiation) along with snow conditions to provide a historical context? Can the controls of sublimation be used to provide more information about how sublimation fluxes may vary? At a minimum, I think more discussion around this topic and how future research can benefit from quantifying the controls of sublimation would be helpful for the paper (i.e. expand on why it is helpful to identify the controlling variables of sublimation).
2) Recent papers including this one have highlighted well that sublimation fluxes become larger and more important as the snow season progresses with the largest sublimation fluxes occurring presumably during the snowmelt period. It would be helpful to provide snow pit and/or snow scale SWE on your depth graphs to provide more context on when the snowmelt season began for this study. The presence of liquid water in the snowpack and particularly near the surface may be an important condition promoting sublimation. Looking at Figure 2 and Figure A1 it appears that there was still a substantial snowpack on 1 June and the analysis removed most of the active snowmelt period following 1 June. While the authors present clear logic for this period of analysis, it would be helpful to comment on how much more sublimation may have occurred during those last few weeks of snowmelt when substantial sublimation rates may have been occurring. Also, further to this point, Table 2 does not highlight a substantial difference in the controls of sublimation (VPD, wind) between the before peak HS and after peak HS period which I would think is another important point of discussion in the paper.
3) Please comment on how you confirmed that only surface sublimation fluxes were being measured and quantified in this study and that measured fluxes do not include blowing snow sublimation from saltation and/or near surface suspension of blowing snow? Although the authors mention the lower wind speeds at the study site compared to other studies, wind speeds do appear to approach 10 m s-1 (Figure 2). Blowing snow can often obscure the sensor path of IRGASON eddy covariance sensors so it would also be helpful to provide comments on how blowing snow may have contributed to the need to gap filling.
Specific comments:
Line 90: change “fall” to “falls”
Lines 90 – 91: Can you provide more detail or perhaps a wind rose diagram highlighting how common winds from the northwest versus the south are at the site? Which condition is more dominant?
Lines 97 – 99, Table 1: Is the 10 min and 2 min measurement frequency for wind speed, radiation, etc. based on an average of a more frequent scan interval by the datalogger (e.g., 15 seconds), or do the meteorological sensors truly only take one measurement every 10 minutes?
Lines 111 – 113: Data gaps – can you comment more on the frequency and length of data gaps that were required to be filled in this study and if there were periods of missing data where simple linear interpolation was not appropriate and rather implementing and diurnal pattern was needed. Were there any thresholds used for the length of missing periods and what techniques were used?
Lines 142 – 143: How were the 10 minutes met data converted to the 15 minute EC flux interval. Also, later used in the partial correlation and XGBoost analysis?
Equation 4: Does this logic make sense to include in the paper since the snow surface temperature was capped at 0 deg C for the data presented (lines 118 – 120)?
Lines 157 – 158: Please provide more detail on the random forest gap filling approach used in this study. Which variables were used as predictors? Also, either here or in the results section, it is important to note how much gap filling was required over the season and during what time periods. Are there certain meteorological conditions that require gap filling? What about conditions during blowing snow? Although the authors briefly mention gap filling in the discussion, more detail is needed in the methods and results.
Lines 212 – 216: Did you consider using vapor pressure of the atmosphere only in your analysis as a separate predictor? Similarly, did you consider using temperature gradient between the atmosphere and snow surface?
Lines 244 – 249: If you prefer not to present the SSG SWE data, can you plot the SWE observations from snowpits along with your snow depth plots? It is important to try to better highlight potential differences between peak snow height and peak SWE, and more specifically when the snowpack was melting. Also, was liquid water content collected in the snowpits? If so, you could also consider also showing those datasets to further strengthen understanding of when the snowpack was melting and how those periods related to sublimation fluxes.
Lines 269 – 271: Consider if this is the best way to phrase this sentence about the residual term. Although you don’t directly measure internal energy changes, in theory you measure to processes driving these internal energy changes during the snow accumulation period when melt/freeze is not occurring.
Lines 312 – 315: Please include more detail on the gap filled periods.
Figure 5: Consider if there is another way to label cumulative sublimation in the legend and caption to avoid confusion that the plot is presenting only gap filled values.
497 – 503: Surprising to see the Stössel et al., 2010 citation being introduced this late in the discussion considering it was conducted at the same study site. Can results from this previous study be more directly compared to the results presented in this work?
506 – 510: Can you comment on if the unmeasured internal energy changes (i.e. residual term) presented in this study is a realistic magnitude based on melt/freeze cycles and internal snow temperature changes?